Week 1, Meeting 1

Part 1: Intro

Technical Skills

  • R (Stats 20)
  • Python (here)
  • add a little bit of SQL (here)
  • Data Wrangling & Cleaning (some here)
  • Data Visualization (some here)
  • Feature Engineering (some here)

Analytical & Statistical Skills

  • Probability & Statistics (not here)
  • Data Exploration (some here)
  • Experimental Design (not here)

Problem-Solving & Soft Skills

  • Python will force you to break down complex problems into smaller more manageable steps,
  • You will also practice thinking logically about designing solutions
  • You will learn to think more like a computer scientist
  • and will also work on data communication & storytelling skills

Today’s First Topic: Stats 21 Tools

  • Please check your Python installation where is yours?
  • Please check for the installation of a proper source code editor (I use VS Code, free + awesome and works on multiple platforms) there are many choices
  • Please install something interactive such as Jupyter Notebook or Lab or Quarto(for homework, presentations, webpages, etc.)
  • and (strongly suggested) check for git and GitHub (do you have an account?)

Check for Python

terminal (MacOS) or powershell/cmd (Windows)

If you don’t know where to find terminal on a Mac, use the magnifying glass and type terminal.

terminal on a mac

powershell in windows

Check for Python

Try this at your terminal prompt or powershell/cmd prompt

Code 
python --version

python version

Checking in Windows

python version use cmd or vscode

Check for a Source Code editor

Visual Studio Code (VS Code)

  • free,
  • lightweight (easy on resources),
  • powerful source code editor VSCode

Show you (some of) the ways

  • Interactive Python REPL (Read-Eval-Print Loop) environment (it’s the OG)
  • A Python Script (like an R script, reusable, for production)
  • Jupyter Notebook (interactive like R Markdown, reusable, for homework and reports, presentations)
  • Quarto (like a Jupyter Notebook on roids, reusable, for reports and presentations)

But first… Checking Installations

Checking Installations in the shell

Python REPL (read-eval-print loop)

Python REPL - just type python at the terminal or cmd prompt

Code from Documentation

Example Code from Documentation

from https://docs.python.org/3/reference/compound_stmts.html#the-for-statement

Result

Example Code Result

Result - note functions use ( )

Example Code Result

Result - note Python’s zero index

Example Code Result

Result - blank line & return (execute)

Example Code Result

Interactive Python Pros and Cons

  • quickly write and test (Python REPL) interactively, which is great for debugging or quick calculations.
  • it can do most anything
  • has access to all libraries and modules
  • works on any operating system
  • cannot save easily, quitting may mean loss
  • clumsy with large programs or large data
  • not well suited for visualizations and tables
  • basic editor (no highlighting, colors etc.)

Another example

Bird Age Calculator

Python script (.py) VSCode Example

Example .py script, moving away from the REPL

Python script (.py) run at the command line

Running the .py script

Python Script Pros and Cons

  • best for large programs
  • used for production
  • well suited for modular programming
  • does everything
  • have access to all libraries and modules
  • works on any operating system
  • works well with Git
  • runs all at once
  • can be automated
  • scalable
  • is used to write your own libraries or modules
  • not ideal for small programs (but works)
  • not ideal for visualizations and tables
  • no immediate feedback
  • must run the entire script to test changes
  • requires an editor or an IDE

By the way

Python modules are collections of scripts. We can see this with our little example:

Import a .py script - but don’t do this, just making a point

Jupyter Notebook

An interactive document that can mix code and text and create web pages and presentations, great for homework

Jupyter Example

This presentation and the code below were written using Quarto

Quarto is not quite as interactive as Jupyter but it can mix code and text and create documents, web pages and presentations, great for homework

Code 
# write a function to calculate human equivalent
def bird_to_human_years(bird_age):
    return bird_age * 5

# generate a sequence perhaps, starts at 1 and excludes 11
ages = range(1,11)

for age in ages:
    print(f"If your bird's age is {age}, in human years your bird is {bird_to_human_years(age)}")
If your bird's age is 1, in human years your bird is 5
If your bird's age is 2, in human years your bird is 10
If your bird's age is 3, in human years your bird is 15
If your bird's age is 4, in human years your bird is 20
If your bird's age is 5, in human years your bird is 25
If your bird's age is 6, in human years your bird is 30
If your bird's age is 7, in human years your bird is 35
If your bird's age is 8, in human years your bird is 40
If your bird's age is 9, in human years your bird is 45
If your bird's age is 10, in human years your bird is 50

What a Quarto Document looks like in VS Code

Quarto is like RMarkdown

Jupyter Notebook/Quarto Pros and Cons

  • does everything
  • has access to all libraries and modules
  • works on any operating system
  • works OK with Git
  • interactive and incremental with immediate feedback
  • can generate beautiful visualizations and tables
  • code and documentation allows us to create notebooks and presentations
  • saves easily
  • requires an editor or IDE
  • not great for large programs
  • not typically used for production
  • not used for writing libraries or modules
  • not well suited for modular programming
  • slower and uses more memory

Optional

Optional: A little bit about git

Try this at your terminal prompt (MacOS) powershell prompt (Windows)

git --version

git version

  • Git is a version control system. GitHub is a hosting platform.
  • Git is primarily used for tracking changes in code by software developers
  • But anyone who can benefit from sharing and tracking changes in code can use it.

Optional: git on Windows

git windows version

Optional: GitHub

GitHub is a collection of tools and services used by software developers which uses git for version control

GitHub

Optional: Using GitHub the Git hosting platform

  • This quarter I will post some material, such as this lecture, to GitHub
  • You could point and click and download
  • BUT it would be better if you knew how to use git on your device to check for updates and download modified versions
  • And that you should learn to manage your own repositories
  • so git is highly recommended but optional for Stats 21

Optional: Quick & Dirty Git & GitHub

If you have your own account with a respository on GitHub, you can modify your repository and access other repositories. You can also keep a copy of the repository on your local computer and interact with the repository stored remotely on GitHub. You really only need 4 commands once your local repository is established

  • Pull (update the local with changes from the remote)
  • Add (stages local updates)
  • Commit (creates a “snapshot” of your repositories just before uploading)
  • Push (uploads the staged and commited changes)

Activity 1

What’s in a name?

  • Please gather together in small groups, please make sure at least one member has a laptop
  • for credit: minimum group size 3, maximum group size 6
  • please introduce yourselves and try to learn a new name today
  • please take a group photo, annotate with with names or add names as a comment during upload
  • upload a photo individually for attendance credit

What to do in your group (10-15 minutes)

  • try to determine whether (a) Python, (b) some kind of code editor (like VS Code, Jupyter Notebook) and git exist on your team’s computers
  • please take some notes for your own personal use if you do not already have these installed (mental note or other)
  • just take a moment to enjoy your time at UCLA

Part 2: A little language example

Let’s Back up a little

  • Where do we start?
  • Maybe an overview using the “Zen of Python” (PEP 20) https://peps.python.org/pep-0020/
  • Then an example
  • Then objects, naming, reserved keywords, etc. see how far we get today.

Opening words of PEP 20

  • Beautiful is better than ugly.
  • Explicit is better than implicit.
  • Simple is better than complex.
  • Complex is better than complicated.
  • Flat is better than nested.
  • Sparse is better than dense.
  • Readability counts.

To understand some decisions regarding “the way” Python does things, understanding their philosophy can help.

Readable, beautiful, self-evident

In Python every word, symbol, and especially indentation matters. An example.

Code 
for count in 0, 1, 2, 3, 4, 5:
    print(count) # Python recommends 4 spaces
0
1
2
3
4
5

If that print(count) is not indented an IndentationError will be generated.

recall (in Stats 20) for takes an instruction (code) and applies it to each item in the iterable, same in Python. A colon denotes the start of an indented code block after which all of the code must be indented by the same amount until the end of the block.

Code 
for spell in "Expelliarmus", "Colloshoo", "Legilimens", "Impedimenta", \
             "Confundo", "Finite Incantatem", "Incarcerous":
    print(spell)
print("This print() is not in the code block")
Expelliarmus
Colloshoo
Legilimens
Impedimenta
Confundo
Finite Incantatem
Incarcerous
This print() is not in the code block

Same general structure, “spell” is the loop variable and the in connects it to the interable. The iterable in this example is a data structure known as a tuple but it is not at all clear to a new user.

spells here is a list (much more about those later) it is another type of data structure. We could also call a list a container object. A list holds other objects.

Code 
spells = [
    "Expelliarmus",
    "Colloshoo",
    "Legilimens",
    "Impedimenta",
    "Confundo", 
    "Finite Incantatem",
    "Incarcerous"
]
for spell in spells:
    print(spell)
Expelliarmus
Colloshoo
Legilimens
Impedimenta
Confundo
Finite Incantatem
Incarcerous

spell is the loop variable and in connects it to spells which is an iterable. We can see that setting up the iterable in this third way is more scalable than the other ways. The object spells can also be reused in other ways and places.

Why more readable, more beautiful, more self-evident?

Previously our iterable object (Ex 1 & 2) was a data structure called a tuple (by implication, not explicit though). An iterable is any object that can be looped over, meaning you can access its elements one by one. While it ran without error, the fact that it is a tuple could have been made more obvious for readability:

Code 
for spell in ("Expelliarmus", "Colloshoo", "Legilimens", "Impedimenta", 
              "Confundo",  "Finite Incantatem", "Incarcerous"):
    print(spell)
Expelliarmus
Colloshoo
Legilimens
Impedimenta
Confundo
Finite Incantatem
Incarcerous

There are many other spells mentioned in the Harry Potter series of books. The iterable here doesn’t scale well. It also isn’t reusable (we cannot easily call it again later in a program)

NOTE/ASIDE

  • We value readable code
  • We value reusable code
  • We value scalable code (e.g., can add more spells easily)
  • We value easy-to-maintain code
  • We value correct code

There is point to this (maybe)

BUT we can re-use the existing list object spells AND we can also avoid the for loop structure (it is slow). ‘\n’ is interpreted as “new line” . In this context ‘\n’ is a separator and is also a string (note the single quotes). The .join() is a string method.

Our code is now very “Pythonic”.

Code 
print('\n'.join(spells))
Expelliarmus
Colloshoo
Legilimens
Impedimenta
Confundo
Finite Incantatem
Incarcerous

Understanding the progression from a simple self-contained for loop, to a one-line implicit iteration using a string joining method is not for today (if you are just starting out). This approach is what you are working towards as an end goal. (Hint read it from it inside out - spells, join, newline)

What is Pythonic?

  • I see it alot, but I don’t think there is an official definition but review PEP 20 if you have the time.

  • PEP 20 describes writing your code in a manner that incorporates the principles and characteristics of the Python programming language.

  • Encourage you to use the concise, expressive, and readable features of Python to write efficient, clear and pretty code

  • I would also add “easy to modify code”

Objects (more later)

  • Everything in Python is an object – numbers, strings, lists, tuples, and functions, these are all objects.
  • Conceptually, an object is like a container or a box with a label on it.
  • Python is at heart an object oriented language but it supports multiple programming models (OOP, Procedural, and Functional)
    • (note) R is a functional programming language at heart
  • Every object is created from some class (more later). A class is a set of instructions or a template for creating objects.
  • Objects have a type and they also have attributes and methods (functions specific to that object).
  • The object oriented nature of Python makes the language very flexible.
  • But also a little tricky coming from R as Python at heart has a very different programming model.

Example: Object Creation

Name and assign values. Very R-like in basic Python:

Code 
x = 5
y = 5
spells = [
    "Expelliarmus",
    "Colloshoo",
    "Legilimens",
    "Impedimenta",
    "Confundo",
    "Finite Incantatem",
    "Incarcerous",
]

z = range(6) # note this is a comment

print(f"This is x: {x}")
print(f"This is y: {y}")
print(f"This is the second spell: {spells[1]}") # Python is zero indexed
print(f"This is z: {z}") # Python is zero indexed
This is x: 5
This is y: 5
This is the second spell: Colloshoo
This is z: range(0, 6)

Objects have… (among other things)

  • ID
  • type
  • value
  • reference count (the number of variables that point to an object in memory)
Code 
print(f"This is the result of id(x): {id(x)}")
print(f"This is the result of id(y): {id(y)}")
print(f"This is the result of id(spells): {id(spells)}")
print(f"This is the result of id(z): {id(z)}")
print(f"This is the result of id(print): {id(print())}")

y = "UCLA"
print(f"This is the result of changing y now id(y): {id(y)}")
This is the result of id(x): 4318683464
This is the result of id(y): 4318683464
This is the result of id(spells): 4363384448
This is the result of id(z): 4426071808

This is the result of id(print): 4318457040
This is the result of changing y now id(y): 4363453328

Example: Object Type & Value

Code 
print(f"This is the result of type(x): {type(x)}")
print(f"This is the result of type(y): {type(y)}")
print(f"This is the result of type(spells): {type(spells)}")
print(f"This is the result of type(z): {type(z)}")
print(f"This is the result of type(print): {type(print)}")
This is the result of type(x): <class 'int'>
This is the result of type(y): <class 'str'>
This is the result of type(spells): <class 'list'>
This is the result of type(z): <class 'range'>
This is the result of type(print): <class 'builtin_function_or_method'>
Code 
print(x)
print(y)
print(spells[1])
print(z)
5
UCLA
Colloshoo
range(0, 6)

This is a range object, the values are not generated until the range object is used.

Code 
for count in z:
    print(count)
0
1
2
3
4
5

Rules for Variable Names

  • They can only consist of:

    • Lowercase letters (a through z)
    • Uppercase letters (A through Z)
    • Digits (0 through 9)
    • The Underscore (_)

  • They are case-sensitive

  • They can begin with a letter or an underscore but not a digit.

  • They cannot be one of Python’s reserved keywords.

Reserved Keywords

  • Please avoid using reserved keyword names as object names.
  • Keywords have specific and predefined meanings.
  • Using them as object names causes confusion and syntax errors.
Code 
import keyword

keywords = keyword.kwlist

for kw in keywords:
    print(kw)
False      await      else       import     pass
None       break      except     in         raise
True       class      finally    is         return
and        continue   for        lambda     try
as         def        from       nonlocal   while
assert     del        global     not        with
async      elif       if         or         yield

Commonly seen keywords

We have already seem some of these on our first day. By the end of the quarter, you should be very familiar with these.

if, else, for, while, 
True, False, def, return, 
import, from, as,
and, elif, in, not, or, pass
class, self, None

https://docs.python.org/3/reference/lexical_analysis.html#keywords

a bit more advanced keywords

We might see a few of these much later in the course.

try, except, finally, raise,  
 del, lambda, with, yield

We will work with the common keywords and try to give you the scaffolding to move to the less common if you need it beyond Stats 21.

Naming (PEP 8)

  • See PEP 8
  • These are the coding conventions for Python for the standard library in the main Python distribution.
  • A worthy code editor will implement most of the rules for you
  • BUT naming is something special to you

Some suggestions for Stats 21

  • Never use the characters ‘l’ (lowercase letter el), ‘O’ (uppercase letter oh), or ‘I’ (uppercase letter eye) as single character variable names.

  • Function names should be lowercase, with words separated by underscores as necessary to improve readability.

  • Variable names follow the same convention as function names.

  • Please capitalize the first letter when defining a Class (e.g., CapitalizedWords)

  • If you create a Python constant please use snake case with all caps (e.g., WEB_ADDRESS)

 DATA_FILE = "data/weather_data.csv"  # a Global constant (file path)

Activity 2

Please try creating some variables

  • Start the interactive interpreter, and type print(42).
  • It should echo 42 on the next line.
  • If you’re still in the interactive interpreter, type 43. It should print 43 on the next line. This is a feature of the interactive interpreter only, and won’t print anything if you’re executing a Python file.
  • Try assigning a value to a variable. Trying printing the value of the variable.
  • Try some illegal names to see what error messages Python tosses back to you.

Part 3

More about type

Some of the basic types
Name Type Mutable? Example
Boolean bool no True; False
Integer int no 3; -256
Floating Point float no 1e5; 2.718
Complex complex no 10j; 2 + 1j
Text str no “UCLA”; “The quick brown fox”
List list yes [“Expelliarmus”, “Confundo”]
Tuple tuple no (0, 2, 4, 6)

Bool

  • The boolean data type values are True and False.

  • In Stats 21 we may use the values directly as data.

  • We may also use them as the evaluation result

Code 
answers = [
    True, True, False, True, False, False, False, True, True, True
    ]

print(f'This is answers: {answers} \n ' ) # F-strings AKA formatted string literals

print(f'Is answers a list object? {isinstance(answers, list)} \n')

print(f'Is False a bool? {isinstance(False, bool)} \n')

print(f'Is 5 < 2? {5 < 2} \n')

print(f'Is Trump > Jesus? {"Trump" > "Jesus"} \n') 
# Strings can be evaluated
# But not in the way you think when the question is nuanced.

print(f'Is UCLA > USC? {"UCLA" > "USC"}\n')

print(f'Is 小 > 大? {"小" > "大"} \n') 
# Computers lack a contextual understanding.

print(f'Unicode of T: {ord("T")}') # how Python evaluates
print(f'Unicode of J: {ord("J")}') # and orders strings

print(f'Unicode of C: {ord("C")}') # U is tied
print(f'Unicode of S: {ord("S")}') # so Python moves to the next letter

print(f'Unicode of 小: {ord("小")}')
print(f'Unicode of 大: {ord("大")}')
This is answers: [True, True, False, True, False, False, False, True, True, True] 
 
Is answers a list object? True 

Is False a bool? True 

Is 5 < 2? False 

Is Trump > Jesus? True 

Is UCLA > USC? False

Is 小 > 大? True 

Unicode of T: 84
Unicode of J: 74
Unicode of C: 67
Unicode of S: 83
Unicode of 小: 23567
Unicode of 大: 22823

Integers

  • Integers are whole numbers, no decimal, no fraction, no comma (use an underscore if you must)
  • could have + or -
Code 
x = 7
y = -7
z = +7
a = 100_000

print(f'x = {x} \n')
print(f'y = {y} \n')
print(f'z = {z} \n')
print(f'a = {a} \n')
x = 7 

y = -7 

z = 7 

a = 100000

Common Operations

Operator Description
+ Addition
- Subtraction
* Multiplication
/ Floating-point division
// Integer division
% Modulus division
** Exponentiation

Examples

Code 
x = 3 + 4 + 5
y = 4 - 1 - 2
z = 2 * 3 * 4 * 5
a = 15/4
b = 15//4
c = 15%4
d = 2**5

print(f'x = 3 + 4 + 5 result is: {x} \n')
print(f'y = 4 - 1 - 2 result is: {y} \n')
print(f'z = 2 * 3 * 4 * 5 result is: {z} \n')
print(f'a = 15/4 result is: {a} \n')
print(f'b = 15//4 result is: {b} \n')
print(f'c = 15%4 result is: {c} \n')
print(f'd = 2**5 result is: {d} \n')
x = 3 + 4 + 5 result is: 12 

y = 4 - 1 - 2 result is: 1 

z = 2 * 3 * 4 * 5 result is: 120 

a = 15/4 result is: 3.75 

b = 15//4 result is: 3 

c = 15%4 result is: 3 

d = 2**5 result is: 32

Notes on operations

  • You can combine literal integers with variables that have been assigned integer values
  • You can modify a variable but you will need to reassign it to store the modification
  • You could use a compound operation e.g., +=, -= to operate and modify at the same time
  • Python uses PEMDAS (Parentheses, Exponents, Multiplication, Division, Addition, and Subtraction)

Examples

Code 
print(f'The result of x + y is: {x + y} \n')
print(f'The result of z/5 is: {z/5} \n')
print(f'But z remains unchanged: {z} \n')
print(f'The current value of y is: {y} \n')
y += 20
print(f'The new value of y is: {y} \n')
The result of x + y is: 13 

The result of z/5 is: 24.0 

But z remains unchanged: 120 

The current value of y is: 1 

The new value of y is: 21

Floats

  • floats in Python have decimal points even if its only zeroes after the decimal
  • floats can include a decimal integer exponent after the letter e
  • The operators work in the same manners as with integers
  • floats are not exact see https://docs.python.org/3/tutorial/floatingpoint.html
Code 
2e10
20000000000.0

Complex (optional)

  • should you need this in the future (not here) Python can work with complex values, they are represented by a j
Code 
x = 2j
y = 10j
z = y/x

print(f'x is {x} \n')
print(f'y is {y} \n')
print(f'y/x = {z} \n')
x is 2j 

y is 10j 

y/x = (5+0j)

Closing

Import

It is the equivalent of library(packagename) in R, it gives us access to helpful extensions. There are different ways

  1. You are bringing the entire pandas library into your code. If you want to use a function from pandas, you will need to write “pandas.” before it for example
import pandas
my_df = pandas.read_csv('mydata.csv')
  1. Still importing the whole pandas library, but using an alias (pd) to save typing. This is most common and accepted way.
import pandas as pd
my_df = pd.read_csv('mydata.csv')
  1. importing one specific function (read_csv) from pandas You can use read_csv directly without typing pandas. or pd. but this is also not commonly seen.
from pandas import read_csv
my_df = read_csv('mydata.csv')

(extra) Using pip and file system commands

  • pip is a package-management system written in Python.
  • I use it to install and manage Python packages but you can use whatever you want.
  • I run pip in the “shell” (terminal on a Mac). The ! allows me to exit Quarto and run commands.
Code 
!pip show numpy
Name: numpy
Version: 2.2.3
Summary: Fundamental package for array computing in Python
Home-page: https://numpy.org
Author: Travis E. Oliphant et al.
Author-email: 
License: Copyright (c) 2005-2024, NumPy Developers.
 All rights reserved.

 Redistribution and use in source and binary forms, with or without
 modification, are permitted provided that the following conditions are
 met:

     * Redistributions of source code must retain the above copyright
        notice, this list of conditions and the following disclaimer.

     * Redistributions in binary form must reproduce the above
        copyright notice, this list of conditions and the following
        disclaimer in the documentation and/or other materials provided
        with the distribution.

     * Neither the name of the NumPy Developers nor the names of any
        contributors may be used to endorse or promote products derived
        from this software without specific prior written permission.

 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
 "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
 LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
 A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
 OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
 SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
 LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
 DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
 THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

 ----

 The NumPy repository and source distributions bundle several libraries that are
 compatibly licensed.  We list these here.

 Name: lapack-lite
 Files: numpy/linalg/lapack_lite/*
 License: BSD-3-Clause
   For details, see numpy/linalg/lapack_lite/LICENSE.txt

 Name: dragon4
 Files: numpy/_core/src/multiarray/dragon4.c
 License: MIT
   For license text, see numpy/_core/src/multiarray/dragon4.c

 Name: libdivide
 Files: numpy/_core/include/numpy/libdivide/*
 License: Zlib
   For license text, see numpy/_core/include/numpy/libdivide/LICENSE.txt


 Note that the following files are vendored in the repository and sdist but not
 installed in built numpy packages:

 Name: Meson
 Files: vendored-meson/meson/*
 License: Apache 2.0
   For license text, see vendored-meson/meson/COPYING

 Name: spin
 Files: .spin/cmds.py
 License: BSD-3
   For license text, see .spin/LICENSE

 Name: tempita
 Files: numpy/_build_utils/tempita/*
 License: MIT
   For details, see numpy/_build_utils/tempita/LICENCE.txt

 ----

 This binary distribution of NumPy also bundles the following software:

 Name: OpenBLAS
 Files: numpy/.dylibs/libscipy_openblas*.so
 Description: bundled as a dynamically linked library
 Availability: https://github.com/OpenMathLib/OpenBLAS/
 License: BSD-3-Clause
   Copyright (c) 2011-2014, The OpenBLAS Project
   All rights reserved.

   Redistribution and use in source and binary forms, with or without
   modification, are permitted provided that the following conditions are
   met:

      1. Redistributions of source code must retain the above copyright
         notice, this list of conditions and the following disclaimer.

      2. Redistributions in binary form must reproduce the above copyright
         notice, this list of conditions and the following disclaimer in
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 Also add information on how to contact you by electronic and paper mail.

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 The hypothetical commands `show w' and `show c' should show the appropriate
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 Name: libquadmath
 Files: numpy/.dylibs/libquadmath*.so
 Description: dynamically linked to files compiled with gcc
 Availability: https://gcc.gnu.org/git/?p=gcc.git;a=tree;f=libquadmath
 License: LGPL-2.1-or-later

     GCC Quad-Precision Math Library
     Copyright (C) 2010-2019 Free Software Foundation, Inc.
     Written by Francois-Xavier Coudert  <fxcoudert@gcc.gnu.org>

     This file is part of the libquadmath library.
     Libquadmath is free software; you can redistribute it and/or
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     License as published by the Free Software Foundation; either
     version 2.1 of the License, or (at your option) any later version.

     Libquadmath is distributed in the hope that it will be useful,
     but WITHOUT ANY WARRANTY; without even the implied warranty of
     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
     Lesser General Public License for more details.
     https://www.gnu.org/licenses/old-licenses/lgpl-2.1.html

 ----

 The NumPy repository and source distributions bundle several libraries that are
 compatibly licensed.  We list these here.

 Name: lapack-lite
 Files: numpy/linalg/lapack_lite/*
 License: BSD-3-Clause
   For details, see numpy/linalg/lapack_lite/LICENSE.txt

 Name: dragon4
 Files: numpy/_core/src/multiarray/dragon4.c
 License: MIT
   For license text, see numpy/_core/src/multiarray/dragon4.c

 Name: libdivide
 Files: numpy/_core/include/numpy/libdivide/*
 License: Zlib
   For license text, see numpy/_core/include/numpy/libdivide/LICENSE.txt


 Note that the following files are vendored in the repository and sdist but not
 installed in built numpy packages:

 Name: Meson
 Files: vendored-meson/meson/*
 License: Apache 2.0
   For license text, see vendored-meson/meson/COPYING

 Name: spin
 Files: .spin/cmds.py
 License: BSD-3
   For license text, see .spin/LICENSE

 Name: tempita
 Files: numpy/_build_utils/tempita/*
 License: MIT
   For details, see numpy/_build_utils/tempita/LICENCE.txt

 ----

 This binary distribution of NumPy also bundles the following software:

 Name: OpenBLAS
 Files: numpy/.dylibs/libscipy_openblas*.so
 Description: bundled as a dynamically linked library
 Availability: https://github.com/OpenMathLib/OpenBLAS/
 License: BSD-3-Clause
   Copyright (c) 2011-2014, The OpenBLAS Project
   All rights reserved.

   Redistribution and use in source and binary forms, with or without
   modification, are permitted provided that the following conditions are
   met:

      1. Redistributions of source code must retain the above copyright
         notice, this list of conditions and the following disclaimer.

      2. Redistributions in binary form must reproduce the above copyright
         notice, this list of conditions and the following disclaimer in
         the documentation and/or other materials provided with the
         distribution.
      3. Neither the name of the OpenBLAS project nor the names of
         its contributors may be used to endorse or promote products
         derived from this software without specific prior written
         permission.

   THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
   AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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   CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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   USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


 Name: LAPACK
 Files: numpy/.dylibs/libscipy_openblas*.so
 Description: bundled in OpenBLAS
 Availability: https://github.com/OpenMathLib/OpenBLAS/
 License: BSD-3-Clause-Attribution
   Copyright (c) 1992-2013 The University of Tennessee and The University
                           of Tennessee Research Foundation.  All rights
                           reserved.
   Copyright (c) 2000-2013 The University of California Berkeley. All
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   Copyright (c) 2006-2013 The University of Colorado Denver.  All rights
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 Name: GCC runtime library
 Files: numpy/.dylibs/libgfortran*, numpy/.dylibs/libgcc*
 Description: dynamically linked to files compiled with gcc
 Availability: https://gcc.gnu.org/git/?p=gcc.git;a=tree;f=libgfortran
 License: GPL-3.0-with-GCC-exception
   Copyright (C) 2002-2017 Free Software Foundation, Inc.

   Libgfortran is free software; you can redistribute it and/or modify
   it under the terms of the GNU General Public License as published by
   the Free Software Foundation; either version 3, or (at your option)
   any later version.

   Libgfortran is distributed in the hope that it will be useful,
   but WITHOUT ANY WARRANTY; without even the implied warranty of
   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
   GNU General Public License for more details.

   Under Section 7 of GPL version 3, you are granted additional
   permissions described in the GCC Runtime Library Exception, version
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   You should have received a copy of the GNU General Public License and
   a copy of the GCC Runtime Library Exception along with this program;
   see the files COPYING3 and COPYING.RUNTIME respectively.  If not, see
   <http://www.gnu.org/licenses/>.

 ----

 Full text of license texts referred to above follows (that they are
 listed below does not necessarily imply the conditions apply to the
 present binary release):

 ----

 GCC RUNTIME LIBRARY EXCEPTION

 Version 3.1, 31 March 2009

 Copyright (C) 2009 Free Software Foundation, Inc. <http://fsf.org/>

 Everyone is permitted to copy and distribute verbatim copies of this
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 This GCC Runtime Library Exception ("Exception") is an additional
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 The availability of this Exception does not imply any general
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 ----

                     GNU GENERAL PUBLIC LICENSE
                        Version 3, 29 June 2007

  Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
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                        TERMS AND CONDITIONS

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             How to Apply These Terms to Your New Programs

   If you develop a new program, and you want it to be of the greatest
 possible use to the public, the best way to achieve this is to make it
 free software which everyone can redistribute and change under these terms.

   To do so, attach the following notices to the program.  It is safest
 to attach them to the start of each source file to most effectively
 state the exclusion of warranty; and each file should have at least
 the "copyright" line and a pointer to where the full notice is found.

     <one line to give the program's name and a brief idea of what it does.>
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     This program is distributed in the hope that it will be useful,
     but WITHOUT ANY WARRANTY; without even the implied warranty of
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     GNU General Public License for more details.

     You should have received a copy of the GNU General Public License
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 Also add information on how to contact you by electronic and paper mail.

   If the program does terminal interaction, make it output a short
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     <program>  Copyright (C) <year>  <name of author>
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     under certain conditions; type `show c' for details.

 The hypothetical commands `show w' and `show c' should show the appropriate
 parts of the General Public License.  Of course, your program's commands
 might be different; for a GUI interface, you would use an "about box".

   You should also get your employer (if you work as a programmer) or school,
 if any, to sign a "copyright disclaimer" for the program, if necessary.
 For more information on this, and how to apply and follow the GNU GPL, see
 <http://www.gnu.org/licenses/>.

   The GNU General Public License does not permit incorporating your program
 into proprietary programs.  If your program is a subroutine library, you
 may consider it more useful to permit linking proprietary applications with
 the library.  If this is what you want to do, use the GNU Lesser General
 Public License instead of this License.  But first, please read
 <http://www.gnu.org/philosophy/why-not-lgpl.html>.

 Name: libquadmath
 Files: numpy/.dylibs/libquadmath*.so
 Description: dynamically linked to files compiled with gcc
 Availability: https://gcc.gnu.org/git/?p=gcc.git;a=tree;f=libquadmath
 License: LGPL-2.1-or-later

     GCC Quad-Precision Math Library
     Copyright (C) 2010-2019 Free Software Foundation, Inc.
     Written by Francois-Xavier Coudert  <fxcoudert@gcc.gnu.org>

     This file is part of the libquadmath library.
     Libquadmath is free software; you can redistribute it and/or
     modify it under the terms of the GNU Library General Public
     License as published by the Free Software Foundation; either
     version 2.1 of the License, or (at your option) any later version.

     Libquadmath is distributed in the hope that it will be useful,
     but WITHOUT ANY WARRANTY; without even the implied warranty of
     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
     Lesser General Public License for more details.
     https://www.gnu.org/licenses/old-licenses/lgpl-2.1.html

Location: /Users/vivian/my_venv313/lib/python3.13/site-packages
Requires: 
Required-by: contourpy, matplotlib, opencv-python, pandas, pydeck, pyogrio, scikit-learn, scipy, seaborn, streamlit

(extra) Using pip and file system commands cont’d

using file system commands (not Python) to calculate size

Code 
!du -sh /Users/vivian/my_venv313/lib/python3.13/site-packages/numpy
!du -sh /Users/vivian/my_venv313/lib/python3.13/site-packages/pandas
!du -sh /Users/vivian/my_venv313/lib/python3.13/site-packages/matplotlib
 57M    /Users/vivian/my_venv313/lib/python3.13/site-packages/numpy
 70M    /Users/vivian/my_venv313/lib/python3.13/site-packages/pandas
 29M    /Users/vivian/my_venv313/lib/python3.13/site-packages/matplotlib